An impact analysis and detection of HTTP flooding attack in cloud using bio-inspired clustering approach

The application layer HTTP flooding attack is the primary threat to web servers hosting web services in the cloud network. Due to varying network changes in the cloud, the traditional security methods are not sufficient to detect the attack. Therefore, a novel approach is proposed, which uses Teacher Learner Based Optimization (TLBO) for clustering to identify the attack requests. In this work, the logs of a web server under attack are collected and pre-processed. Further, Principal Component Analysis (PCA) is used to reduce the dimensionality of the pre-processed data. Thereafter the data is clustered using TLBO clustering, which will separate the application layer HTTP flooding attack in one cluster and rest of the requests in the other cluster. The results prove that the proposed approach performs better than other traditional and bio-inspired clustering techniques. The proposed approach also attains the peak detection rate and lowermost false alarm, which proves the efficacy of the proposed approach among another state of the art approaches.